RapidOCRSharpOnnx 1.1.1

RapidOCRSharpOnnx

🚀a high performance, cross-platform PaddleOCR C# inference library base on OpenCV and ONNX Runtime. Referring to the RapidOCR project, it is a python version of the C# implementation with a redesigned and optimized architecture.

Features

• Supported Languages PP-OCRv5 provides multilingual text recognition capabilities covering 106 languages, please refer to the documentation: PP-OCRv5 Multilingual Text Recognition.
• Execution Provider CPU, CUDA / TensorRT, OpenVINO, CoreML, DirectML
• Batch processing images Preprocess and Inference are executed asynchronously with Producer/Consumer pattern
• High Performance Inference Memory reuse, GPU Inference with I/O Binding
• Image Processing OpenCvSharp4
• Draw Result Image SkiaSharp
• Inference Engine ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
• PP-OCR Versions Includes support for: PP-OCRv5, PP-OCRv4

Example Images:

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Usage

1. Export model to ONNX format:

For convert the pre-trained PP-OCR model to ONNX format, please refer to the the documentation: Obtaining ONNX Models, or download from rapid-ocr Model List.

2. Load the ONNX model with C#:

Install Nuget packages RapidOCRSharpOnnx, OnnxRuntime, OpenCvSharp4.runtime

CPU Inference

dotnet add package RapidOCRSharpOnnx
dotnet add package OpenCvSharp4.runtime.win
dotnet add package Microsoft.ML.OnnxRuntime

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderCPU(new OcrConfig(detectPath, recPath, LangRec.CH, OCRVersion.PPOCRV5, clsMobilePath)));

CoreML Inference

dotnet add package RapidOCRSharpOnnx
dotnet add package OpenCvSharp4.runtime.osx.10.15-x64
dotnet add package Microsoft.ML.OnnxRuntime

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderCoreML(new OcrConfig(detectPath, recPath, LangRec.CH, OCRVersion.PPOCRV5, clsMobilePath)));

CUDA/TensorRT Inference

dotnet add package RapidOCRSharpOnnx
dotnet add package OpenCvSharp4.runtime.win
dotnet add package Microsoft.ML.OnnxRuntime.Gpu.Windows

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderCUDA(new OcrConfig(detectPath, recogPath, LangRec.CH, OCRVersion.PPOCRV5, clsPath), deviceId));
using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderTensorRT(new OcrConfig(detectPath, recogPath, LangRec.CH, OCRVersion.PPOCRV5, clsPath), deviceId));

DirectML Inference

dotnet add package RapidOCRSharpOnnx
dotnet add package OpenCvSharp4.runtime.win
dotnet add package Microsoft.ML.OnnxRuntime.DirectML

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderDirectML(new OcrConfig(detectPath, recogPath, LangRec.EN, OCRVersion.PPOCRV5, clsPath), deviceId));

OpenVINO Inference

dotnet add package RapidOCRSharpOnnx
dotnet add package OpenCvSharp4.runtime.win
dotnet add package Intel.ML.OnnxRuntime.OpenVino

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderOpenVINO(new OcrConfig(detectPath, recogPath, LangRec.EN, OCRVersion.PPOCRV5, clsPath), IntelDeviceType.NPU));

Use the following C# code to load the model and run basic prediction

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderDirectML(new OcrConfig(detectPath, recogPath, LangRec.EN, OCRVersion.PPOCRV5, clsPath), _deviceId));
string savePath = $"res_{Path.GetFileName(imgPath)}";
var result = ocr.RecognizeText(imgPath, savePath);
Console.WriteLine($"result: {result.ToString()}");

Batch processing images

 using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderDirectML(new OcrConfig(detectPath, recogPath, LangRec.CH, OCRVersion.PPOCRV5, clsPath), _deviceId));
 var list = Directory.GetFiles(@"C:\code\model\OCRTestImages");
 Stopwatch sw = new Stopwatch();
 sw.Start();
 var resPath = ocr.BatchParallelAsync(list.ToList(), saveDir, receiveAction: ReceiveResult);
 sw.Stop();
 Console.WriteLine($"BatchAsync Time: {sw.ElapsedMilliseconds} ms");

private static void ReceiveResult(OcrBatchResult batchResult)
{
    Console.WriteLine(batchResult.ToString());
    Console.WriteLine("------------------------------------------------------------");
}

Batch processing images foreach api

using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderDirectML(new OcrConfig(detectPath, recogPath, LangRec.CH, OCRVersion.PPOCRV5, clsPath), _deviceId));
var list = Directory.GetFiles(@"D:\code\model\OCRTestImages");
var resPaths = ocr.BatchForeachAsync(list.ToList(), @"D:\code\model\OCRTestImagesResults");

await foreach (var item in resPaths)
{
    Console.WriteLine(item.TextBlocks);
}

Custom font to draw result image

private static void TestImage()
{
     string imgPath = @"E:\Hp\ai-image\ADFtools\headerText.png";
     string detectPath = @"D:\code\RapidOCRSharpOnnx\RapidOCRSharpOnnx.TestCommon\Models\ch_PP-OCRv5_det_mobile.onnx";
     string recogPath = @"D:\code\RapidOCRSharpOnnx\RapidOCRSharpOnnx.TestCommon\Models\ch_PP-OCRv5_rec_mobile.onnx";
     string clsPath = @"D:\code\RapidOCRSharpOnnx\RapidOCRSharpOnnx.TestCommon\Models\ch_PP-LCNet_x0_25_textline_ori_cls_mobile.onnx";

     string font = @"C:\Windows\Fonts\msyh.ttc";
     using RapidOCRSharp ocr = new RapidOCRSharp(new ExecutionProviderDirectML(new OcrConfig(detectPath, recogPath, font, OCRVersion.PPOCRV5, clsPath), _deviceId));

     string savePath = $"res_{Path.GetFileName(imgPath)}";
     var result = ocr.RecognizeText(imgPath, savePath);
     Console.WriteLine($"result: {result.ToString()}");
}

Performance Test

Performance Test PC

Performance Test Data

Images: 60 images (image size: 1180x92)

PP-OCR Model: ch_PP-OCRv5_det_mobile, ch_PP-OCRv5_rec_mobile, ch_PP-LCNet_x0_25_textline_ori_cls_mobile

PaddleSharp test result

CPU inference time: 48.1769278s

<img width="1331" height="847" alt="image" src="https://github.com/user-attachments/assets/87515077-e9c2-48b2-9e43-6b145e1c7a7a" />

RapidOCRSharpOnnx test result

CPU inference time: 9.2447871s

<img width="1477" height="874" alt="image" src="https://github.com/user-attachments/assets/eee41ffe-f0a7-48af-b934-74e290ee6196" />

PaddleOCR test result

CPU inference time: 62.668516899924725s

<img width="1009" height="911" alt="image" src="https://github.com/user-attachments/assets/b367749f-3c37-4326-bb30-ea4fcb52315d" />

RapidOCR test result

CPU inference time: 17.963430000003427 s

<img width="997" height="1093" alt="image" src="https://github.com/user-attachments/assets/79ab7dd5-0311-42ea-aa03-ed5695ff5fae" />

Performance Test Result